4.1 Basic Concepts
4.2 Frequent Itemset Mining Methods
4.3 Which Patterns Are Interesting?
Pattern Evaluation Methods
4.4 Summary
Chapter 4: Mining Frequent Patterns,
Associations and Correlations
Frequent Pattern Analysis
Frequent Pattern:
a pattern (a set of items, subsequences,
substructures, etc.) that occurs frequently in a data set
Goal:
finding inherent regularities in data
What products were often purchased together?
—
Beer and
diapers?!
What are the subsequent purchases after buying a PC?
What kinds of DNA are sensitive to this new drug?
Can we automatically classify Web documents?
Applications:
Basket data analysis, cross

marketing, catalog design, sale
campaign analysis, Web log (click stream) analysis, and DNA
sequence analysis.
Why is Frequent Pattern Mining Important?
An important property of datasets
Foundation for many essential data mining tasks
Association, correlation, and causality analysis
Sequential, structural (e.g., sub

graph) patterns
Pattern analysis in spatiotemporal, multimedia, time

series, and
stream data
Classification: discriminative, frequent pattern analysis
Clustering analysis: frequent pattern

based clustering
Data warehousing: iceberg cube and cube

gradient
Semantic data compression
Broad applications
Frequent Patterns
itemset:
A set of one or more items
K

itemset
X = {x
1
, …, x
k
}
(absolute) support
, or,
support count
of X: Frequency or occurrence of an
itemset X
(relative)
support
,
s
, is the fraction of
transactions that contains X (i.e., the
probability
that a transaction
contains X)
An itemset X is
frequent
if X’s support
is no less than a
minsup
threshold
Customer
buys diaper
Customer
buys both
Customer
buys beer
Tid
Items bought
10
Beer, Nuts, Diaper
20
Beer, Coffee, Diaper
30
Beer, Diaper, Eggs
40
Nuts, Eggs, Milk
50
Nuts, Coffee, Diaper, Eggs, Milk
Association Rules
Find all the rules
X
Y
with minimum
support and confidence
threshold
support
, s, probability that a
transaction contains X
Y
confidence
,
c, conditional
probability that a transaction having
X also contains Y
Let
minsup
= 50%,
minconf
= 80%
Freq. Pat.: Beer:3, Nuts:3, Diaper:4,
Eggs:3, {Beer, Diaper}:3
Association rules: (many more!)
Beer
Diaper (60%, 100%)
Diaper
Beer (60%, 75%)
Rules that satisfy both
minsup
and
minconf
are called
strong rules
Customer
buys diaper
Customer
buys both
Customer
buys beer
Tid
Items bought
10
Beer, Nuts, Diaper
20
Beer, Coffee, Diaper
30
Beer, Diaper, Eggs
40
Nuts, Eggs, Milk
50
Nuts, Coffee, Diaper, Eggs, Milk
Closed Patterns and Max

Patterns
A long pattern contains a combinatorial number of sub

patterns,
e.g.,
{a
1
, …, a
100
}
contains
2
100
–
1
=
1.27
*
10
30
sub

patterns!
Solution:
Mine
closed patterns
and
max

patterns
instead
An
itemset
X
is
closed
if X is
frequent
and there exists
no super

pattern
Y
כ
X,
with the same support
as X
An
itemset
X is a
max

pattern
if X is frequent and there exists no
frequent super

pattern Y
כ
X
Closed pattern is a lossless compression of freq. patterns
Reducing the number of patterns and rules
Closed Patterns and Max

Patterns
Example
DB = {<a
1
, …, a
100
>, < a
1
, …, a
50
>}
Min_sup
=1
What is the set of
closed
itemset
?
<a1, …, a100>: 1
< a1, …, a50>: 2
What is the set of
max

pattern
?
<a1, …, a100>: 1
What is the set of
all patterns
?
!!
Computational Complexity
How
many itemsets are potentially to be generated in the worst
case?
The number of frequent itemsets to be generated is sensitive to the
minsup threshold
When minsup is low, there exist potentially an exponential number of
frequent itemsets
The worst
case: MN where M: # distinct items, and N: max length
of transactions
4.1 Basic Concepts
4.2 Frequent Itemset Mining Methods
4.2.1
Apriori: A Candidate Generation

and

Test Approach
4.2.2
Improving the Efficiency of Apriori
4.2.3
FPGrowth: A Frequent Pattern

Growth Approach
4.2.4
ECLAT: Frequent Pattern Mining with Vertical Data Format
4.3 Which Patterns Are Interesting?
Pattern Evaluation Methods
4.4 Summary
Chapter
4
: Mining Frequent Patterns,
Associations and Correlations
4.2.1Apriori: Concepts and Principle
The
downward closure
property of frequent patterns
Any subset of a frequent itemset must be frequent
If {beer, diaper, nuts} is frequent, so is {beer, diaper}
i.e., every transaction having {beer, diaper, nuts} also contains
{beer, diaper}
Apriori pruning principle:
If there is
any
itemset which is infrequent,
its superset should not be generated/tested
4.2.1Apriori: Method
Initially, scan DB once to get frequent 1

itemset
Generate
length (k+1)
candidate
itemsets from length k frequent
itemsets
Test
the candidates against DB
Terminate when no frequent or candidate set can be generated
Apriori: Example
Database
1
st
scan
C
1
L
1
L
2
C
2
C
2
2
nd
scan
C
3
L
3
3
rd
scan
Tid
Items
10
A, C, D
20
B, C, E
30
A, B, C, E
40
B, E
Itemset
sup
{A}
2
{B}
3
{C}
3
{D}
1
{E}
3
Itemset
sup
{A}
2
{B}
3
{C}
3
{E}
3
Itemset
{A, B}
{A, C}
{A, E}
{B, C}
{B, E}
{C, E}
Itemset
sup
{A, B}
1
{A, C}
2
{A, E}
1
{B, C}
2
{B, E}
3
{C, E}
2
Itemset
sup
{A, C}
2
{B, C}
2
{B, E}
3
{C, E}
2
Itemset
{B, C, E}
Itemset
sup
{B, C, E}
2
Sup
min
=
2
Apriori Algorithm
C
k
: Candidate itemset of size k
L
k
: frequent itemset of size k
L
1
= {frequent items};
for
(
k
= 1;
L
k
!=
;
k
++)
do begin
C
k+1
= candidates generated from
L
k
;
for each
transaction
t
in database
do
increment the count of all candidates in C
k+1
that are
contained in t
L
k+1
= candidates in
C
k+1
with min_support
end
return
k
L
k
;
Candidate Generation
How to generate candidates?
Step 1: self

joining
L
k
Step 2: pruning
Example of Candidate Generation
L
3
={
abc
,
abd
,
acd
, ace,
bcd
}
Self

joining: L
3
*L
3
:
abcd
from
abc
,
abd
, and
bcd
,
acde
from
acd
and
ace
Pruning:
acde
is removed because
ade
is not in
L
3
C4 = {
abcd
}
4.2.2 Generating Association Rules
Once the frequent itemsets have been found, it is straightforward
to generate
strong
association rules that satisfy:
minimum
support
minimum
confidence
Relation between support and confidence:
support_count(A
B
)
Confidence(A
B
) 㴠倨B籁)㴠
††††††††††††††††††††††††††††
獵sp潲o_捯畮c(A)
Support_count(A
B
⤠
is the number of transactions containing the
itemsets A
B
Support_count(A)
is the number of transactions containing the
itemset A.
Generating Association Rules
For each frequent itemset
L
, generate all non empty subsets of
L
For
every non empty subset
S
of
L
,
output the rule
:
S
⡌

匩
†
If (support_count(L)/support_count(S)) >= min_conf
Example
TID
List of item IDS
T100
I1,I2,I5
T200
I2,I4
T300
I2,I3
T400
I1,I2,I4
T500
I1,I3
T600
I2,I3
T700
I1,I3
T800
I1,I2,I3,I5
T900
I1,I2,I3
Suppose the frequent
Itemset
L={I1,I2,I5}
Subsets of L are:
{I1,I2},
{I1,I5},{I2,I5},{I1},{I2},{I5}
Association rules :
I1
I2
䤵
confidence = 2/4= 50%
I1
I5
䤲
confidence=2/2=100%
I2
I5
䤱
confidence=2/2=100%
I1
I2
I5
confidence=2/6=33%
I2
I1
I5
confidence=2/7=29%
I5
I2
I2
confidence=2/2=100%
If the minimum confidence =70%
Transactional Database
4.2.2
Improving the Efficiency of Apriori
Major computational challenges
Huge number of candidates
Multiple scans of transaction database
Tedious workload of support counting for candidates
Improving Apriori: general ideas
Shrink number of candidates
Reduce passes of transaction database scans
Facilitate support counting of candidates
(A) DHP: Hash

based Technique
Making a hash table
10: {A,C}, {A, D}, {C,D}
20: {B,C}, {B, E}, {C,E}
30: {A,B}, {A, C}, {A,E}, {B,C}, {B, E}, {C,E}
40: {B, E}
3
1
2
0
3
0
2
0
1
2
3
4
5
6
{C,E}
{C,E}
{A, B}
{A,E}
{B,C}
{B,C}
{B,E}
{B,E}
{B,E}
{A,C}
{A,C}
Hash codes
Buckets
Buckets
counters
min

support=2
We have the
following
binary vector
1 0 1 0 1 0 1
{A
,B}
1
{A, C}
2
{B,C}
2
{B, E}
3
{C,E}
2
{A, C}
{B,C}
{B, E}
{C,E}
J. Park, M. Chen, and P. Yu. An effective hash

based algorithm for mining association rules. SIGMOD’95
Database
1
st
scan
C
1
Tid
Items
10
A, C, D
20
B, C, E
30
A, B, C, E
40
B, E
Itemset
sup
{A}
2
{B}
3
{C}
3
{D}
1
{E}
3
(B) Partition: Scan Database Only Twice
Subdivide the transactions of
D
into
k
non overlapping partitions
Any itemset that is potentially frequent in
D
must be frequent in at
least one of the partitions
Di
Each partition can fit into main memory, thus it is read only once
Steps:
Scan1: partition database and find local frequent patterns
Scan2: consolidate global frequent patterns
A. Savasere, E. Omiecinski and S. Navathe, VLDB’95
D
1
D
2
D
k
+
= D
+
+
(C) Sampling for Frequent Patterns
Select a sample of the original database
Mine frequent patterns within the sample using Apriori
Use a lower support threshold than the minimum support to find
local frequent itemsets
Scan the database once to verify the frequent itemsets found in
the sample
Only broader frequent patterns are checked
Example: check abcd instead of ab, ac,…, etc.
Scan the database again to find missed frequent patterns
H. Toivonen. Sampling large databases for association rules. In VLDB’
96
(D) Dynamic: Reduce Number of Scans
S. Brin R. Motwani, J. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket
data. In SIGMOD’97
ABCD
ABC
ABD
ACD
BCD
AB
AC
BC
AD
BD
CD
A
B
C
D
{}
Itemset lattice
Transactions
1

itemsets
2

itemsets
…
Apriori
1

itemsets
2

items
3

items
DIC
Once both A and D are determined
frequent, the counting of AD begins
Once all length

2 subsets of BCD are
determined frequent, the counting of
BCD begins
4.2.3 FP

growth: Frequent Pattern

Growth
Adopts a divide and conquer strategy
Compress the database representing frequent items into a
frequent
–
pattern tree
or
FP

tree
Retains the itemset association information
Divid the compressed database into a set of conditional
databases, each associated with one frequent item
Mine each such databases separately
Example: FP

growth
The first scan of data is the
same as Apriori
Derive the set of frequent 1

itemsets
Let min

sup=2
Generate a set of ordered
items
TID
List of item IDS
T100
I1,I2,I5
T200
I2,I4
T300
I2,I3
T400
I1,I2,I4
T500
I1,I3
T600
I2,I3
T700
I1,I3
T800
I1,I2,I3,I5
T900
I1,I2,I3
Transactional Database
Item ID
Support
count
I2
7
I1
6
I3
6
I4
2
I5
2
Construct the FP

Tree
Transactional Database
Item ID
Support
count
I2
7
I1
6
I3
6
I4
2
I5
2
null

Create a branch for each
transaction

Items in each transaction are
processed in order
1

Order the items T100: {I2,I1,I5}
2

Construct the first branch:
<I2:1>, <I1:1>,<I5:1>
TID
Items
TID
Items
TID
Items
T100
I1,I2,I5
T400
I1,I2,I4
T700
I1,I3
T200
I2,I4
T500
I1,I3
T800
I1,I2,I3,I5
T300
I2,I3
T600
I2,I3
T900
I1,I2,I3
I2:1
I1:1
I5:1
Construct the FP

Tree
Transactional Database
Item ID
Support
count
I2
7
I1
6
I3
6
I4
2
I5
2
null

Create a branch for each
transaction

Items in each transaction are
processed in order
1

Order the items T200: {I2,I4}
2

Construct the second branch:
<I2:1>, <I4:1>
TID
Items
TID
Items
TID
Items
T100
I1,I2,I5
T400
I1,I2,I4
T700
I1,I3
T200
I2,I4
T500
I1,I3
T800
I1,I2,I3,I5
T300
I2,I3
T600
I2,I3
T900
I1,I2,I3
I2:1
I1:1
I5:1
I4:1
I2:
2
Construct the FP

Tree
Transactional Database
Item ID
Support
count
I2
7
I1
6
I3
6
I4
2
I5
2
null

Create a branch for each
transaction

Items in each transaction are
processed in order
1

Order the items T300: {I2,I3}
2

Construct the third branch:
<I2:2>, <I3:1>
TID
Items
TID
Items
TID
Items
T100
I1,I2,I5
T400
I1,I2,I4
T700
I1,I3
T200
I2,I4
T500
I1,I3
T800
I1,I2,I3,I5
T300
I2,I3
T600
I2,I3
T900
I1,I2,I3
I2:2
I1:1
I5:1
I4:1
I3:1
I2:
3
Construct the FP

Tree
Transactional Database
Item ID
Support
count
I2
7
I1
6
I3
6
I4
2
I5
2
null

Create a branch for each
transaction

Items in each transaction are
processed in order
1

Order the items T400: {I2,I1,I4}
2

Construct the fourth branch:
<I2:3>, <I1:1>,<I4:1>
TID
Items
TID
Items
TID
Items
T100
I1,I2,I5
T400
I1,I2,I4
T700
I1,I3
T200
I2,I4
T500
I1,I3
T800
I1,I2,I3,I5
T300
I2,I3
T600
I2,I3
T900
I1,I2,I3
I1:1
I5:1
I4:1
I3:1
I2:3
I4:1
I1:
2
I2:
4
Construct the FP

Tree
Transactional Database
Item ID
Support
count
I2
7
I1
6
I3
6
I4
2
I5
2
null

Create a branch for each
transaction

Items in each transaction are
processed in order
1

Order the items T400: {I1,I3}
2

Construct the fifth branch:
<I1:1>, <I3:1>
TID
Items
TID
Items
TID
Items
T100
I1,I2,I5
T400
I1,I2,I4
T700
I1,I3
T200
I2,I4
T500
I1,I3
T800
I1,I2,I3,I5
T300
I2,I3
T600
I2,I3
T900
I1,I2,I3
I1:2
I5:1
I4:1
I3:1
I2:4
I4:1
I1:1
I3:1
Construct the FP

Tree
Transactional Database
Item ID
Support
count
I2
7
I1
6
I3
6
I4
2
I5
2
null
TID
Items
TID
Items
TID
Items
T100
I1,I2,I5
T400
I1,I2,I4
T700
I1,I3
T200
I2,I4
T500
I1,I3
T800
I1,I2,I3,I5
T300
I2,I3
T600
I2,I3
T900
I1,I2,I3
I1:4
I5:1
I4:1
I3:2
I2:7
I4:1
I1:2
I3:2
I3:2
I5:1
When a branch of a
transaction is added, the
count for each node
along a common prefix is
incremented by 1
Construct the FP

Tree
Item ID
Support
count
I2
7
I1
6
I3
6
I4
2
I5
2
null
I1:4
I5:1
I4:1
I3:2
I2:7
I4:1
I1:2
I3:2
I3:2
I5:1
The problem of mining frequent patterns in databases is
transformed to that of mining the FP

tree
Construct the FP

Tree

Occurrences of I5:
<I2,I1,I5> and <I2,I1,I3,I5>

Two prefix Paths
<I2, I1: 1> and <I2,I1,I3: 1>

Conditional FP tree contains only
<I2: 2, I1: 2>, I3 is not
considered because its support count of 1 is less than the
minimum support count.

Frequent patterns
{I2,I5:2}, {I1,I5:2},{I2,I1,I5:2}
Item ID
Support
count
I2
7
I1
6
I3
6
I4
2
I5
2
null
I1:4
I5:1
I4:1
I3:2
I2:7
I4:1
I1:2
I3:2
I3:2
I5:1
Construct the FP

Tree
Item ID
Support
count
I2
7
I1
6
I3
6
I4
2
I5
2
null
I1:4
I5:1
I4:1
I3:2
I2:7
I4:1
I1:2
I3:2
I3:2
I5:1
TID
Conditional Pattern Base
Conditional FP

tree
I5
{{I2,I1:
1
},{I2,I1,I3:
1
}}
<I2:
2
,I1:
2
>
I4
{{I2,I1:
1
},{I2,
1
}}
<I2:
2
>
I3
{{I2,I1:
2
},{I2:
2
}, {I1:
2
}}
<I2:
4
,I1:
2
>,<I1:
2
>
I1
{I2,
4
}
<I2:
4
>
Construct the FP

Tree
Item ID
Support
count
I2
7
I1
6
I3
6
I4
2
I5
2
null
I1:4
I5:1
I4:1
I3:2
I2:7
I4:1
I1:2
I3:2
I3:2
I5:1
TID
Conditional FP

tree
Frequent Patterns Generated
I5
<I2:
2
,I1:
2
>
{I2,I5:2}, {I1,I5:2},{I2,I1,I5:2}
I4
<I2:
2
>
{I2,I4:2}
I3
<I2:
4
,I1:
2
>,<I1:
2
>
{I2,I3:4},{I1,I3:4},{I2,I1,I3:2}
I1
<I2:
4
>
{I2,I1:4}
FP

growth properties
FP

growth transforms the problem of finding long frequent patterns
to searching for shorter once recursively and concatenating the
suffix
It uses the least frequent suffix offering a good selectivity
It reduces the search cost
If the tree does not fit into main memory, partition the database
Efficient and scalable for mining both long and short frequent
patterns
4.2.4 ECLAT: FP Mining with Vertical Data Format
Both
Apriori
and
FP

growth
use
horizontal data format
Alternatively data can also be represented in
vertical format
TID
List of item IDS
T100
I 1,I 2,I 5
T200
I 2,I 4
T300
I 2,I 3
T400
I 1,I 2,I 4
T500
I 1,I 3
T600
I 2,I 3
T700
I 1,I 3
T800
I 1,I 2,I 3,I 5
T900
I 1,I 2,I 3
itemset
TID_set
I 1
{T100,T400,T500,T700,T800,T900}
I 2
{T100,T200,T300,T400,T600,T800,T900}
I 3
{T300,T500,T600,T700,T800,T900}
I 4
{T200,T400}
I 5
{T100,T800}
ECLAT Algorithm by Example
Transform the horizontally formatted data to the vertical format
by scanning the database once
The support count of an itemset is simply the length of the TID_set
of the itemset
TID
List of item IDS
T100
I 1,I 2,I 5
T200
I 2,I 4
T300
I 2,I 3
T400
I 1,I 2,I 4
T500
I 1,I 3
T600
I 2,I 3
T700
I 1,I 3
T800
I 1,I 2,I 3,I 5
T900
I 1,I 2,I 3
itemset
TID_set
I 1
{T100,T400,T500,T700,T800,T900}
I 2
{T100,T200,T300,T400,T600,T800,T900}
I 3
{T300,T500,T600,T700,T800,T900}
I 4
{T200,T400}
I 5
{T100,T800}
ECLAT Algorithm by Example
The frequent k

itemsets can be used to construct the candidate
(k+1)

itemsets based on the Apriori property
itemset
TID_set
I 1
{T100,T400,T500,T700,T800,T900}
I 2
{T100,T200,T300,T400,T600,T800,T900}
I 3
{T300,T500,T600,T700,T800,T900}
I 4
{T200,T400}
I 5
{T100,T800}
Frequent 1

itemsets in vertical format
min_sup=2
Frequent 2

itemsets in vertical format
itemset
TID_set
{I 1,I2}
{T100,T400,T800,T900}
{I 1,I3}
{T500,T700,T800,T900}
{I 1,I4}
{T400}
{I 1,I5}
{T100,T800}
{I 2,I3}
{T300,T600,T800,T900}
{I 2,I4}
{T200,T400}
{I 2,I5}
{T100,T800}
{I 3,I5}
{T800}
ECLAT Algorithm by Example
This process repeats, with k incremented by 1 each time, until no
frequent items or no candidate itemsets can be found
Properties of mining with vertical data format
Take the advantage of the Apriori property in the generation of
candidate (k+1)

itemset from k

itemsets
No need to scan the database to find the support of (k+1)
itemsets, for k>=1
The TID_set of each k

itemset carries the complete information
required for counting such support
The TID

sets can be quite long, hence expensive to manipulate
Use
diffset
technique to optimize the support count computation
itemset
TID_set
{I 1,I2,I3}
{T800,T900}
{I 1,I2,I5}
{T100,T800}
Frequent 3

itemsets in vertical format
min_sup=2
4.1 Basic Concepts
4.2 Frequent Itemset Mining Methods
4.2.1
Apriori: A Candidate Generation

and

Test Approach
4.2.2
Improving the Efficiency of Apriori
4.2.3
FPGrowth: A Frequent Pattern

Growth Approach
4.2.4
ECLAT: Frequent Pattern Mining with Vertical Data Format
4.3 Which Patterns Are Interesting?
Pattern Evaluation Methods
4.4 Summary
Chapter 4: Mining Frequent Patterns,
Associations and Correlations
Strong Rules Are Not Necessarily Interesting
Whether a rule is interesting or not can be assesses either
subjectively or objectively
Objective interestingness measures can be used as one step
toward the goal of finding interesting rules for the user
Example of a misleading “strong” association rule
Analyze transactions of AllElectronics data about computer
games and videos
Of the
10,000
transactions analyzed
6,000
of the transactions include
computer games
7,500
of the transactions include
videos
4,000
of the transactions include
both
Suppose that min_sup=30% and min_confidence=60%
The following association rule is discovered:
Buys(X, “computer games”)
buys(X,
“videos”
)[support =40%, confidence=66%]
Strong Rules Are Not Necessarily Interesting
Buys(X, “computer games”)
buys(X,
“videos”
)[support 40%, confidence=66%]
This rule is strong but it is misleading
The probability of purshasing videos is
75%
which is even larger
than
66%
In fact computer games and videos are negatively associated
because the purchase of one of these items actually decreases
the likelihood of purchasing the other
The confidence of a rule
A
䈠
can be deceiving
It is only an estimate of the conditional probability of itemset
B
given itemset
A
.
It does not measure the real strength of the correlation implication
between
A
and
B
Need to use
Correlation Analysis
From Association to Correlation Analysis
Use
Lift
, a simple correlation measure
The occurrence of itemset
A
is independent of the occurrence of
itemset
B
if
P(A
B
)=P(䄩P(B)
, otherwise itemsets
A
and
B
are
dependent and correlated as events
The lift between the occurences of
A
and
B
is given by
Lift(A,B)=P(A
B
)/P(䄩P(B)
If > 1, then A and B are positively correlated (the occurrence of one
implies the occurrence of the other)
If <1, then A and B are negatively correlated
If =1, then A and B are independent
Example: P({game, video})=0.4/(0.60
0.75
)=0.89
4.1 Basic Concepts
4.2 Frequent Itemset Mining Methods
4.2.1
Apriori: A Candidate Generation

and

Test Approach
4.2.2
Improving the Efficiency of Apriori
4.2.3
FPGrowth: A Frequent Pattern

Growth Approach
4.2.4
ECLAT: Frequent Pattern Mining with Vertical Data Format
4.3 Which Patterns Are Interesting?
Pattern Evaluation Methods
4.4 Summary
Chapter 4: Mining Frequent Patterns,
Associations and Correlations
Basic Concepts:
association rules, support

confident framework,
closed and max patterns
Scalable frequent pattern mining methods
Apriori (Candidate generation & test)
Projection

based (FPgrowth)
Vertical format approach (ECLAT)
Interesting Patterns
Correlation analysis
4.4 Summary
Applications and Tools in Data Mining
1. Financial Data Analysis
Banks and Institutions offer a wise variety of banking services
Checking and savings accounts for business or individual
customers
Credit business, mortgage, and automobile loans
Investment services (mutual funds)
Insurance services and stock investment services
Financial data is relatively complete, reliable, and of high
quality
What to do with this data?
1. Financial Data Analysis
Design of data warehouses for multidimensional data analysis
and data mining
Construct
data warehouses
(data come from different sources)
Multidimensional Analysis
: e.g., view the revenue changes by
month. By region, by sector, etc. along with some statistical
information such as the mean, the average, the maximum and
the minimum values, etc.
Characterization and class comparison
Outlier analysis
1. Financial Data Analysis
Loan Payment Prediction and costumer credit policy analysis
Attribute selection and attribute relevance ranking may help
indentifying important factors and eliminate irrelevant ones
Example of factors related to the risk of loan payment
Term of the loan
Debt ratio
Payment to income ratio
Customer level income
Education level
Residence region
The bank can adjust its decisions
according to the subset of factors selected (use classification)
2. Retail Industry
Collect huge amount of data on sales, customer
shopping history, goods transportation,
consumption and service, etc.
Many stores have web sites where you can buy
online. Some of them exist only online (e.g.,
Amazon)
Data mining helps to
Identify costumer buying behaviors
Discover customers shopping patterns and trends
Improve the quality of costumer service
Achieve better costumer satisfaction
Design more effective good transportation
Reduce the cost of business
2. Retail Industry
Design
data warehouses
Multidimensional
analysis
Analysis of the
effectiveness of sales campaigns
Advertisements, coupons, discounts, bonuses, etc
Comparing transactions that contain sales items
during and after the campaign
Costumer retention
Analyze the change in costumers behaviors
Product
Recommendation
Mining association rules
Display associative information to promote sales
3. Telecommunication Industry
Many different ways of communicating
Fax, cellular phone, Internet messenger, images, e

mail, computer and Web data transmission, etc.
Great demand of data mining to help
Understanding the business involved
Indentifying telecommunication patterns
Catching fraudulent activities
Making better use of resources
Improve the quality of service
3. Telecommunication Industry
Multidimensional
analysis (several attributes)
Several features: Calling time, Duration, Location of
caller, Location of callee, Type of call, etc.
Compare data traffic, system workload, resource
usage, user group behavior, and profit
Fraudulent Pattern Analysis
Identify potential fraudulent users
Detect attempts to gain fraudulent entry to costumer
accounts
Discover unusual patterns (outlier analysis)
4. Many Other Applications
Biological
Data Analysis
E.g., identification and analysis of human genomes
and other species
Web
Mining
E.g., explore linkage between web pages to compute
authority scores (Page Rank Algorithm)
Intrusion detection
Detect any action that threaten file integrity,
confidentiality, or availability of a network resource
How to Choose a Data Mining System (Tool)?
Do data mining systems share the same well defined operations
and a standard query language?
No
Many commercial data mining systems have a little in common
Different functionalities
Different methodology
Different data sets
You need to carefully choose the data mining system that is
appropriate for your task
How to Choose a Data Mining System (Tool)?
Data Types and Sources
Available systems handle formatted record

based, relational

like
data with numerical, and nominal attributes
That data could be on the form of ASCII text, relational databases,
or data warehouse data
It is important to check which kind of data the system you are
choosing can handle
It is important that the data mining system supports ODBC
connections (Open Database Connectivity)
Operating System
A data mining system may run only on one operating system
The most popular operating systems that host data mining tools
are UNIX/LINUX and Microsoft Windows
How to Choose a Data Mining System (Tool)?
Data Mining functions and Methodologies
Some systems provide only one data mining function(e.g.,
classification). Other system support many functions
For a given data mining function (e.g., classification), some
systems support only one method. Other systems may support
many methods (k

nearest neighbor, naive Bayesian, etc.)
Data mining system should provide default settings for non experts
How to Choose a Data Mining System (Tool)?
Coupling data mining with databases(data warehouse) systems
No Coupling
A DM system will not use any function of a DB/DW system
Fetch data from particular resource (file)
Process data and then store results in a file
Loose coupling
A DM system use some facilities of a DB/DW system
Fetch data from data repositories managed by a DB/DW
Store results in a file or in the DB/DW
Semi

tight coupling
Efficient implementation of few essential data mining primitives
(sorting, indexing, histogram analysis) is provided by the DB/DW
Tight coupling
A DM system is smoothly integrated into the DB/DW
Data mining queries are optimized
Tight coupling is highly desirable because it facilitates
implementations and provide high system performance
How to Choose a Data Mining System (Tool)?
Scalability
Query execution time should increase linearly with the number of
dimensions
Visualization
“A picture is worth a thousand words”
The quality and the flexibility of visualization tools may strongly
influence usability, interpretability and attractiveness of the system
Data Mining Query Language and Graphical user Interface
High quality user interface
It is not common to have a query language in a DM system
Examples of Commercial Data Mining Tools
Database system and graphics vendors
Intelligent Miner (IBM)
Microsoft SQL Server 2005
MineSet (Purple Insight)
Oracle Data Mining (ODM)
Examples of Commercial Data Mining Tools
Vendors of statistical analysis or data mining software
Clementine (SPSS)
Enterprise Miner (SAS Institute)
Insightful Miner (Insightful Inc.)
Examples of Commercial Data Mining Tools
Machine learning community
CART (Salford Systems)
See5 and C5.0 (RuleQuest)
Weka developed at the university Waikato (open source)
End of The Data Mining Course
Questions? Suggestions?
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